Thermochemical pathways coupled with carbon capture for valorizing animal manure: A review
Bibliographic record
Abstract
Livestock manure is a significant source of methane and nitrous oxide emissions, posing serious environmental challenges. While conventional management often exacerbates these impacts, thermochemical technologies such as pyrolysis, gasification, and hydrothermal processing offer promising pathways to convert manure into energy, biochar, and other valuable products within a circular bioeconomy. However, these processes can still emit carbon, limiting their environmental benefits. Integrating carbon capture technologies can mitigate these emissions, enabling net-negative outcomes and enhancing overall energy efficiency. This review explores the technical, economic, and environmental aspects of thermochemical manure valorization and emphasizes the transformative potential of coupling these processes with carbon capture. Combustion and co-firing reduce greenhouse gas emissions compared to fossil fuels, but are hindered by manure’s high ash and moisture content. Advanced thermochemical methods such as pyrolysis and gasification yield biochar, bio-oil, and syngas, yet face similar limitations. Hydrothermal processing, especially hydrothermal liquefaction and carbonization, effectively addresses moisture-related challenges and is particularly effective for pig manure. Catalytic gasification further improves conversion efficiency and product quality but remains costly due to expensive catalysts and process complexity. Evidence suggests that integrating carbon capture with thermochemical conversion offers a viable solution for achieving net-negative emissions. However, the widespread deployment of these integrated systems is constrained by high capital costs and infrastructure requirements. Realizing their full potential requires targeted investment, technological innovation, and robust policy support to overcome existing barriers and drive scalable, sustainable implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".